Ez-texting Python API Docs | dltHub

EzTexting is a cloud‑based SMS marketing platform that offers a REST API for sending texts, managing contacts, and accessing analytics. The REST API base URL is https://app.eztexting.com and Authentication is performed by supplying User and Password parameters with each request..

dlt is an open-source Python library that handles authentication, pagination, and schema evolution automatically. dlthub provides AI context files that enable code assistants to generate production-ready pipelines. Install with uv pip install "dlt[workspace]" and start loading Ez-texting data in under 10 minutes.

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What data can I load from Ez-texting?

Here are some of the endpoints you can load from Ez-texting:

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How do I authenticate with the Ez-texting API?

Each request must include the User and Password parameters, either in the query string (GET) or as form fields (POST). No additional headers are required.

1. Get your credentials

1. Log in to your EzTexting account at https://app.eztexting.com.
2. Click on your account name in the top‑right corner and select Account Settings.
3. Choose API Access or Developer Settings from the sidebar.
4. Your API username (User) and password are displayed there; copy them for use in dlt configuration.
5. If no credentials are shown, use the Create New API Key button to generate a new User/Password pair.

2. Add them to .dlt/secrets.toml

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[sources.ez_texting_source]
user = "your_user_here"
password = "your_password_here"

dlt reads this automatically at runtime — never hardcode tokens in your pipeline script. For production environments, see setting up credentials with dlt for environment variable and vault-based options.

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How do I set up and run the pipeline?

Set up a virtual environment and install dlt:

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uv venv && source .venv/bin/activate
uv pip install "dlt[workspace]"

1. Install the dlt AI Workbench:

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dlt ai init --agent <your-agent> # <agent>: claude | cursor | codex

This installs project rules, a secrets management skill, appropriate ignore files, and configures the dlt MCP server for your agent. Learn more →

2. Install the rest-api-pipeline toolkit:

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dlt ai toolkit rest-api-pipeline install

This loads the skills and context about dlt the agent uses to build the pipeline iteratively, efficiently, and safely. The agent uses MCP tools to inspect credentials — it never needs to read your secrets.toml directly. Learn more →

3. Start LLM-assisted coding:

Use /find-source to load data from the Ez-texting API into DuckDB.

The rest-api-pipeline toolkit takes over from here — it reads relevant API documentation, presents you with options for which endpoints to load, and follows a structured workflow to scaffold, debug, and validate the pipeline step by step.

4. Run the pipeline:

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python ez_texting_pipeline.py

If everything is configured correctly, you'll see output like this:

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Pipeline ez_texting_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset ez_texting_data
The duckdb destination used duckdb:/ez_texting.duckdb location to store data
Load package 1749667187.541553 is LOADED and contains no failed jobs

Inspect your pipeline and data:

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dlt pipeline ez_texting_pipeline show

This opens the Pipeline Dashboard where you can verify pipeline state, load metrics, schema (tables, columns, types), and query the loaded data directly.

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Python pipeline example

This example loads contacts and incoming_messages from the Ez-texting API into DuckDB. It mirrors the endpoint and data selector configuration from the table above:

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import dlt
from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources

@dlt.source
def ez_texting_source(user=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://app.eztexting.com",
            "auth": {
                "type": "api_key",
                "api_key": user,
            },
        },
        "resources": [
            {"name": "contacts", "endpoint": {"path": "contacts", "data_selector": "Response.Entries"}},
            {"name": "incoming_messages", "endpoint": {"path": "incoming-messages", "data_selector": "Response.Entries"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="ez_texting_pipeline",
        destination="duckdb",
        dataset_name="ez_texting_data",
    )
    load_info = pipeline.run(ez_texting_source())
    print(load_info)

To add more endpoints, append entries from the resource table to the "resources" list using the same name, path, and data_selector pattern.

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How do I query the loaded data?

Once the pipeline runs, dlt creates one table per resource. You can query with Python or SQL.

Python (pandas DataFrame):

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import dlt

data = dlt.pipeline("ez_texting_pipeline").dataset()
sessions_df = data.contacts.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM ez_texting_data.contacts LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("ez_texting_pipeline").dataset()
data.contacts.df().head()

See how to explore your data in marimo Notebooks and how to query your data in Python with dataset.

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What destinations can I load Ez-texting data to?

dlt supports loading into any of these destinations — only the destination parameter changes:

Change the destination in dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. See the full destinations list.

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Troubleshooting

Authentication Errors

• 401 Unauthorized – Returned when the User and Password parameters are missing, incorrect, or disabled. The response body contains:

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{ "Response": { "Status": "Failure", "Code": 401, "Errors": ["Authorization Required"] } }

• 403 Forbidden – Indicates the authenticated user does not have permission to access the requested resource.

Resource Errors

• 404 Not Found – The requested endpoint or resource ID does not exist.
• 500 Internal Server Error – An unexpected server‑side error; retry after a short delay.

Pagination

Most list endpoints support page and limit query parameters. If omitted, the API returns the first page with a default limit. Verify the presence of Response.TotalPages and Response.Page in the response to handle pagination correctly.

Ensure that the API key is valid to avoid 401 Unauthorized errors. Also, verify endpoint paths and parameters to avoid 404 Not Found errors.

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Next steps

Continue your data engineering journey with the other toolkits of the dltHub AI Workbench:

• data-exploration — Build custom notebooks, charts, and dashboards for deeper analysis with marimo notebooks.
• dlthub-runtime — Deploy, schedule, and monitor your pipeline in production.

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dlt ai toolkit data-exploration install
dlt ai toolkit dlthub-runtime install

• How to deploy a pipeline
• How to explore your data in marimo Notebooks
• How to query your data in Python with dataset
• Join the dlt community on Slack